Freya: an event prediction model for power distribution networks

Abstract

In the Smart Grids (SGs) context, energy utilities manage extensive data volumes to monitor and optimize distribution networks. Key parameters include voltage, current, and power levels, as well as fault indicators like short-circuit currents and voltage sags. Detecting and predicting technical failures is for preventing power shortages that affect residential and industrial consumers. The process of predicting future conditions of the SG, is essential for identifying potential technical failures. SGs feature hierarchically distributed equipment across large geographical areas, influencing the overall network based on their hierarchical importance andinevice-level predictions to forecast the network’s overall state—finally, the process of inferences through OntoFreya, the ontology proposed in this thesis. OntoFreya classifiedividual historical contexts. Continuous communication between these devices and monitoring centers is vital for effective SG operation. Integrating concepts such as Edge Computing (EC), the Internet of Things (IoT), and Machine Learning (ML) enhances event prediction and operational efficiency in SGs.This thesis introduces the Freya model, an intelligent computational framework designed for event prediction in SGs, focusing on energy distribution. Freya’s scientific contribution lies in event prediction at both the equipment and network levels. Comparative analysis shows that Freya uniquely addresses three aspects: (1) operation of remote equipment, (2) context-aware on SGs, and (3) hierarchical importance within the network. These aspects serve as inputs for predictive modeling.Event prediction in Freya consists of three steps. Initially, ML models are applied to individual SG devices. Subsequently, a stacked ML model consolidates these ds network and equipment events in compliance with energy utility regulations and regulatory standards, enabling proactive maneuvers to mitigate potential issues. The model’s validation uses real-world data from distribution feeders, voltage regulators, reclosers, and various applied scenarios, demonstrating the capability of the Freya model. The Freya model for distribution networks achieved an accuracy of 99.73%, recall of 99.75%, and F1- Score of 99.73%, compared to commonly used models in this type of task, which reached an accuracy of 83.36%, recall of 82.91%, and F1-Score of 83.36%, demonstrating the superiority of the Freya model in terms of event prediction metrics.